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Related Experiment Video

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward

Yusuke Sakemi, Kai Morino, Takashi Morie

IEEE Transactions on Neural Networks and Learning Systems

|July 19, 2021

Summary

This summary is machine-generated.

We introduce a new supervised learning algorithm for Spiking Neural Networks (SNNs) that uses temporal coding for energy-efficient analog VLSI implementation. This method achieves high accuracy on image recognition tasks, comparable to existing SNN algorithms.

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Area of Science:

Artificial Intelligence
Neuroscience
Computer Engineering

Background:

Spiking Neural Networks (SNNs) are brain-inspired models processing information via spikes.
SNNs offer potential for novel machine learning and energy-efficient computation in Very-Large-Scale Integration (VLSI) circuits.

Purpose of the Study:

To propose a novel supervised learning algorithm for SNNs utilizing temporal coding.
To design spiking neurons for ultra-high energy efficiency in analog VLSI with resistive memory.
To enhance recognition task performance and assess robustness against manufacturing variations.

Main Methods:

Developed a supervised learning algorithm for SNNs based on temporal coding.
Designed spiking neurons optimized for analog VLSI and resistive memory.
Implemented techniques to improve recognition performance and robustness.

Main Results:

Achieved classification accuracy comparable to state-of-the-art SNN algorithms on MNIST and Fashion-MNIST datasets.
Demonstrated the potential for ultra-high energy efficiency through analog VLSI implementation.
Evaluated and discussed the robustness of SNNs against device manufacturing variations.

Conclusions:

The proposed temporal coding SNN algorithm offers high performance and energy efficiency for analog VLSI.
The algorithm shows promise for robust image recognition despite manufacturing variations.
This work contributes to the development of efficient and practical SNNs.